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Elena Antonio - One of the best experts on this subject based on the ideXlab platform.
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Ventilatory drive and the Apnea-Hypopnea Index in six-to-twelve year old children
BMC Pulmonary Medicine, 2004Co-Authors: Ralph F Fregosi, Stuart F Quan, Andrew C Jackson, Kris L Kaemingk, Wayne J Morgan, Jamie L Goodwin, Jenny C Reeder, Rosaria K Cabrera, Elena AntonioAbstract:Background We tested the hypothesis that ventilatory drive in hypoxia and hypercapnia is inversely correlated with the number of Hypopneas and obstructive Apneas per hour of sleep (obstructive Apnea Hypopnea Index, OAHI) in children. Methods Fifty children, 6 to 12 years of age were studied. Participants had an in-home unattended polysomnogram to compute the OAHI. We subsequently estimated ventilatory drive in normoxia, at two levels of isocapnic hypoxia, and at three levels of hyperoxic hypercapnia in each subject. Experiments were done during wakefulness, and the mouth occlusion pressure measured 0.1 seconds after inspiratory onset (P_0.1) was measured in all conditions. The slope of the relation between P_0.1 and the partial pressure of end-tidal O_2 or CO_2 (P_ETO_2 and P_ETCO_2) served as the Index of hypoxic or hypercapnic ventilatory drive. Results Hypoxic ventilatory drive correlated inversely with OAHI (r = -0.31, P = 0.041), but the hypercapnic ventilatory drive did not (r = -0.19, P = 0.27). We also found that the resting P_ETCO_2 was significantly and positively correlated with the OAHI, suggesting that high OAHI values were associated with resting CO_2 retention. Conclusions In awake children the OAHI correlates inversely with the hypoxic ventilatory drive and positively with the resting P_ETCO_2. Whether or not diminished hypoxic drive or resting CO_2 retention while awake can explain the severity of sleep-disordered breathing in this population is uncertain, but a reduced hypoxic ventilatory drive and resting CO_2 retention are associated with sleep-disordered breathing in 6–12 year old children.
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Ventilatory drive and the Apnea-Hypopnea Index in six-to-twelve year old children
BMC pulmonary medicine, 2004Co-Authors: Ralph F Fregosi, Stuart F Quan, Andrew C Jackson, Kris L Kaemingk, Wayne J Morgan, Jamie L Goodwin, Jenny C Reeder, R. Cabrera, Elena AntonioAbstract:We tested the hypothesis that ventilatory drive in hypoxia and hypercapnia is inversely correlated with the number of Hypopneas and obstructive Apneas per hour of sleep (obstructive Apnea Hypopnea Index, OAHI) in children. Fifty children, 6 to 12 years of age were studied. Participants had an in-home unattended polysomnogram to compute the OAHI. We subsequently estimated ventilatory drive in normoxia, at two levels of isocapnic hypoxia, and at three levels of hyperoxic hypercapnia in each subject. Experiments were done during wakefulness, and the mouth occlusion pressure measured 0.1 seconds after inspiratory onset (P0.1) was measured in all conditions. The slope of the relation between P0.1 and the partial pressure of end-tidal O2 or CO2 (PETO2 and PETCO2) served as the Index of hypoxic or hypercapnic ventilatory drive. Hypoxic ventilatory drive correlated inversely with OAHI (r = -0.31, P = 0.041), but the hypercapnic ventilatory drive did not (r = -0.19, P = 0.27). We also found that the resting PETCO2 was significantly and positively correlated with the OAHI, suggesting that high OAHI values were associated with resting CO2 retention. In awake children the OAHI correlates inversely with the hypoxic ventilatory drive and positively with the resting PETCO2. Whether or not diminished hypoxic drive or resting CO2 retention while awake can explain the severity of sleep-disordered breathing in this population is uncertain, but a reduced hypoxic ventilatory drive and resting CO2 retention are associated with sleep-disordered breathing in 6–12 year old children.
Juha Toyras - One of the best experts on this subject based on the ideXlab platform.
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Intra-night variation in Apnea-Hypopnea Index affects diagnostics and prognostics of obstructive sleep Apnea.
Sleep & breathing = Schlaf & Atmung, 2019Co-Authors: Sami Nikkonen, Esa Mervaala, Juha Toyras, Sami Myllymaa, Philip I. Terrill, Timo LeppanenAbstract:Diagnostics of obstructive sleep Apnea (OSA) is based on Apnea-Hypopnea Index (AHI) determined as full-night average of occurred events. We investigate our hypothesis that intra-night variation in the frequency of obstructive events affects diagnostics and prognostics of OSA and should therefore be considered in clinical practice. Polygraphic recordings of 1989 patients (mean follow-up 18.3 years) with suspected OSA were analyzed. Number and severity of individual obstructive events were calculated hourly for the first 6 h of sleep. OSA severity was determined based on the full-night AHI and AHI for the 2 h when the obstructive event frequency was highest (AHI2h). Hazard ratios for all-cause, cardiovascular, and non-cardiovascular mortalities were calculated for different OSA severity categories based on the full-night AHI and AHI2h. Frequency and duration of obstructive events varied hour-by-hour increasing towards morning. Using AHI2h led to a statistically significant rearrangement of patients between the OSA severity categories. The use of AHI2h for severity classification showed clearer relationship between the OSA severity and mortality than the full-night AHI. Currently, the intra-night variation in frequency and severity of obstructive events is completely ignored by conventional, full-night AHI and considering this information could improve the diagnostics of OSA.
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mortality risk based Apnea Hypopnea Index thresholds for diagnostics of obstructive sleep Apnea
Journal of Sleep Research, 2019Co-Authors: Henri Korkalainen, Juha Toyras, Sami Nikkonen, Timo LeppanenAbstract:: The severity of obstructive sleep Apnea is clinically assessed mainly using the Apnea-Hypopnea Index. Based on the Apnea-Hypopnea Index, patients are classified into four severity groups: non-obstructive sleep Apnea (Apnea-Hypopnea Index < 5); mild (5 ≤ Apnea-Hypopnea Index < 15); moderate (15 ≤ Apnea-Hypopnea Index < 30); and severe obstructive sleep Apnea (Apnea-Hypopnea Index ≥ 30). However, these thresholds lack solid clinical and scientific evidence. We hypothesize that the current Apnea-Hypopnea Index thresholds are not optimal despite their global use, and aim to assess this clinical shortcoming by optimizing the thresholds with respect to the risk of all-cause mortality. We analysed ambulatory polygraphic recordings of 1,783 patients with suspected obstructive sleep Apnea (mean follow-up 18.3 years). We simulated 79,079 different threshold combinations in 100 randomized subgroups of the population and studied the relative risk of all-cause mortality corresponding to each combination and randomization. The optimal thresholds were chosen according to three criteria: (a) the hazard ratios increase linearly between severity groups towards more severe obstructive sleep Apnea; (b) each group includes at least 15% of the study population; (c) group sizes decrease with increasing obstructive sleep Apnea severity. The risk of all-cause mortality varied greatly across simulations; the threshold defining non-obstructive sleep Apnea group having the largest effect on the hazard ratios. The Apnea-Hypopnea Index threshold combination of 3-9-24 was optimal in most of the subgroups. In conclusion, the assessment of obstructive sleep Apnea severity based on the current Apnea-Hypopnea Index thresholds is not optimal. Our novel approach provides methods for optimizing Apnea-Hypopnea Index-based severity classification, and the revised thresholds better differentiate patients into severity groups, ensuring that an increase in the severity corresponds to an increase in the risk of all-cause mortality.
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adjustment of Apnea Hypopnea Index with severity of obstruction events enhances detection of sleep Apnea patients with the highest risk of severe health consequences
Sleep and Breathing, 2014Co-Authors: Anu Murajamurro, Pekka Tiihonen, T. Hukkanen, Esa Mervaala, Juha Toyras, Antti Kulkas, Mikko Hiltunen, Salla KupariAbstract:Introduction Presently, the severity of obstructive sleep Apnea (OSA) is estimated based on the Apnea-Hypopnea Index (AHI). Unfortunately, AHI does not provide information on the severity of individual obstruction events. Previously, the severity of individual obstruction events has been suggested to be related to the outcome of the disease. In this study, we incorporate this information into AHI and test whether this novel approach would aid in discriminating patients with the highest risk. We hypothesize that the introduced adjusted AHI parameter provides a valuable supplement to AHI in the diagnosis of the severity of OSA.
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Total duration of Apnea and Hypopnea events and average desaturation show significant variation in patients with a similar Apnea-Hypopnea Index.
Journal of Medical Engineering & Technology, 2012Co-Authors: Anu Muraja-murro, Pekka Tiihonen, Jouko Nurkkala, T. Hukkanen, Henri Tuomilehto, J Kokkarinen, Esa Mervaala, Juha ToyrasAbstract:Obstructive sleep Apnea (OSA) is commonly diagnosed based on the Apnea-Hypopnea Index (AHI). Presently, novel indices were introduced for sleep Apnea severity: total duration of sleep Apnea and Hypopnea events (TAHD%) and a combined Index including duration and severity of the events (TAHD% × average desaturation). Two hundred and sixty-seven subjects were divided based on their AHI into four categories (normal, mild, moderate, severe OSA). In the most severe cases TAHD% exceeded 70% of the recorded time. This is important as excessive TAHD% may increase mortality and cerebro-vascular complications. Moreover, simultaneous increase in duration and frequency of Apnea and Hypopnea events leads to a paradoxical situation where AHI cannot increase along severity of the disease. Importantly, the combined Index including duration and severity of the events showed significant variation between patients with similar Apnea-Hypopnea indices. To conclude, the present results suggest that the novel parameters could gi...
Kwang Suk Park - One of the best experts on this subject based on the ideXlab platform.
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Apnea Hypopnea Index prediction using electrocardiogram acquired during the sleep onset period
IEEE Transactions on Biomedical Engineering, 2017Co-Authors: Dawoon Jung, Su Hwan Hwang, Doun Jeong, Kwang Suk ParkAbstract:The most widely used methods for predicting obstructive sleep Apnea are based on clinical or anatomico-functional features. To improve exactitude in obstructive sleep Apnea screening, this study aimed to devise a new predictor of Apnea–Hypopnea Index. We hypothesized that less irregular respiration cycles would be observed in the patients with more severe obstructive sleep Apnea during the sleep-onset period. From each of the 156 and 70 single-lead electrocardiograms collected from the internal polysomnographic database and from the Physionet Apnea-ECG database, respectively, the 150-s sleep-onset period was determined and the respiration cycles during this period were detected. Using the coefficient of variation of the respiration cycles, obtained from the internal dataset, as a predictor, the Apnea–Hypopnea Index predictive model was developed through regression analyses and k-fold cross-validations. The Apnea–Hypopnea Index predictability of the regression model was tested with the Physionet Apnea-ECG database. The regression model trained and validated from the 143 and 13 data, respectively, produced an absolute error (mean ± SD) of $3.65 \pm 2.98$ events/h and a Pearson's correlation coefficient of 0.97 ( P < 0.01) between the Apnea–Hypopnea Index predictive values and the reference values for the 70 test data. The new predictor of Apnea–Hypopnea Index has the potential to be utilized in making more reasoned clinical decisions on the need for formal diagnosis and treatment of obstructive sleep Apnea. Our study is the first study that presented the strategy for providing a reliable Apnea–Hypopnea Index without overnight recording.
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Apnea-Hypopnea Index prediction through an assessment of autonomic influence on heart rate in wakefulness
Physiology & behavior, 2016Co-Authors: Dawoon Jung, Doun Jeong, Yu Jin Lee, Kwang Suk ParkAbstract:Abstract With the high prevalence of obstructive sleep Apnea, the issue of developing a practical tool for obstructive sleep Apnea screening has been raised. Conventional obstructive sleep Apnea screening tools are limited in their ability to help clinicians make rational decisions due to their inability to predict the Apnea-Hypopnea Index. Our study aimed to develop a new prediction model that can provide a reliable Apnea-Hypopnea Index value during wakefulness. We hypothesized that patients with more severe obstructive sleep Apnea would exhibit more attenuated waking vagal tone, which may result in lower effectiveness in decreasing heart rate as a response to deep inspiration breath-holding. Prior to conducting nocturnal in-laboratory polysomnography, 30 non-obstructive sleep Apnea (Apnea-Hypopnea Index k -fold cross-validation tests were performed to develop an Apnea-Hypopnea Index prediction model. For the remaining 92 individuals, the developed model provided an absolute error (mean ± SD) of 3.53 ± 2.67 events/h and a Pearson's correlation coefficient of 0.99 ( P
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Apnea–Hypopnea Index Prediction Using Electrocardiogram Acquired During the Sleep-Onset Period
IEEE transactions on bio-medical engineering, 2016Co-Authors: Dawoon Jung, Su Hwan Hwang, Doun Jeong, Yu Jin Lee, Kwang Suk ParkAbstract:The most widely used methods for predicting obstructive sleep Apnea are based on clinical or anatomico-functional features. To improve exactitude in obstructive sleep Apnea screening, this study aimed to devise a new predictor of Apnea–Hypopnea Index. We hypothesized that less irregular respiration cycles would be observed in the patients with more severe obstructive sleep Apnea during the sleep-onset period. From each of the 156 and 70 single-lead electrocardiograms collected from the internal polysomnographic database and from the Physionet Apnea-ECG database, respectively, the 150-s sleep-onset period was determined and the respiration cycles during this period were detected. Using the coefficient of variation of the respiration cycles, obtained from the internal dataset, as a predictor, the Apnea–Hypopnea Index predictive model was developed through regression analyses and k-fold cross-validations. The Apnea–Hypopnea Index predictability of the regression model was tested with the Physionet Apnea-ECG database. The regression model trained and validated from the 143 and 13 data, respectively, produced an absolute error (mean ± SD) of $3.65 \pm 2.98$ events/h and a Pearson's correlation coefficient of 0.97 ( P < 0.01) between the Apnea–Hypopnea Index predictive values and the reference values for the 70 test data. The new predictor of Apnea–Hypopnea Index has the potential to be utilized in making more reasoned clinical decisions on the need for formal diagnosis and treatment of obstructive sleep Apnea. Our study is the first study that presented the strategy for providing a reliable Apnea–Hypopnea Index without overnight recording.
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Apnea-Hypopnea Index estimation using quantitative analysis of sleep macrostructure.
Physiological Measurement, 2016Co-Authors: Dawoon Jung, Su Hwan Hwang, Un Jeong, Kwang Suk ParkAbstract:Obstructive sleep Apnea, characterized by recurrent cessation or substantial reduction in breathing during sleep, is a prevalent and serious medical condition. Although a significant relationship between obstructive sleep Apnea and sleep macrostructure has been revealed in several studies, useful applications of this relationship have been limited. The aim of this study was to suggest a novel approach using quantitative analysis of sleep macrostructure to estimate the Apnea–Hypopnea Index, which is commonly used to assess obstructive sleep Apnea. Without being bound by conventional sleep macrostructure parameters, various new sleep macrostructure parameters were extracted from the polysomnographic recordings of 132 subjects. These recordings were split into training and validation sets, each with 66 recordings including 48 recordings with an Apnea–Hypopnea Index greater than 5 events h−1. The nonlinear regression analysis, performed using the percentage transition probability from non-rapid eye movement sleep stage 2 to stage 1, was most effective in estimating the Apnea–Hypopnea Index. Between the Apnea–Hypopnea Index estimates and the reference values reported from polysomnography, a root mean square error of 7.30 events h−1 was obtained in the validation set. At an Apnea–Hypopnea Index cut-off of ≥30 events h−1, the obstructive sleep Apnea diagnostic performance was provided with a sensitivity of 90.0%, a specificity of 93.5%, and an accuracy of 92.4% by our method. The developed Apnea–Hypopnea Index estimation model has the potential to be utilized in circumstances in which it is not possible to acquire or analyze respiration signal but it is possible to obtain information on sleep macrostructure.
Ralph F Fregosi - One of the best experts on this subject based on the ideXlab platform.
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Ventilatory drive and the Apnea-Hypopnea Index in six-to-twelve year old children
BMC Pulmonary Medicine, 2004Co-Authors: Ralph F Fregosi, Stuart F Quan, Andrew C Jackson, Kris L Kaemingk, Wayne J Morgan, Jamie L Goodwin, Jenny C Reeder, Rosaria K Cabrera, Elena AntonioAbstract:Background We tested the hypothesis that ventilatory drive in hypoxia and hypercapnia is inversely correlated with the number of Hypopneas and obstructive Apneas per hour of sleep (obstructive Apnea Hypopnea Index, OAHI) in children. Methods Fifty children, 6 to 12 years of age were studied. Participants had an in-home unattended polysomnogram to compute the OAHI. We subsequently estimated ventilatory drive in normoxia, at two levels of isocapnic hypoxia, and at three levels of hyperoxic hypercapnia in each subject. Experiments were done during wakefulness, and the mouth occlusion pressure measured 0.1 seconds after inspiratory onset (P_0.1) was measured in all conditions. The slope of the relation between P_0.1 and the partial pressure of end-tidal O_2 or CO_2 (P_ETO_2 and P_ETCO_2) served as the Index of hypoxic or hypercapnic ventilatory drive. Results Hypoxic ventilatory drive correlated inversely with OAHI (r = -0.31, P = 0.041), but the hypercapnic ventilatory drive did not (r = -0.19, P = 0.27). We also found that the resting P_ETCO_2 was significantly and positively correlated with the OAHI, suggesting that high OAHI values were associated with resting CO_2 retention. Conclusions In awake children the OAHI correlates inversely with the hypoxic ventilatory drive and positively with the resting P_ETCO_2. Whether or not diminished hypoxic drive or resting CO_2 retention while awake can explain the severity of sleep-disordered breathing in this population is uncertain, but a reduced hypoxic ventilatory drive and resting CO_2 retention are associated with sleep-disordered breathing in 6–12 year old children.
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Ventilatory drive and the Apnea-Hypopnea Index in six-to-twelve year old children
BMC pulmonary medicine, 2004Co-Authors: Ralph F Fregosi, Stuart F Quan, Andrew C Jackson, Kris L Kaemingk, Wayne J Morgan, Jamie L Goodwin, Jenny C Reeder, R. Cabrera, Elena AntonioAbstract:We tested the hypothesis that ventilatory drive in hypoxia and hypercapnia is inversely correlated with the number of Hypopneas and obstructive Apneas per hour of sleep (obstructive Apnea Hypopnea Index, OAHI) in children. Fifty children, 6 to 12 years of age were studied. Participants had an in-home unattended polysomnogram to compute the OAHI. We subsequently estimated ventilatory drive in normoxia, at two levels of isocapnic hypoxia, and at three levels of hyperoxic hypercapnia in each subject. Experiments were done during wakefulness, and the mouth occlusion pressure measured 0.1 seconds after inspiratory onset (P0.1) was measured in all conditions. The slope of the relation between P0.1 and the partial pressure of end-tidal O2 or CO2 (PETO2 and PETCO2) served as the Index of hypoxic or hypercapnic ventilatory drive. Hypoxic ventilatory drive correlated inversely with OAHI (r = -0.31, P = 0.041), but the hypercapnic ventilatory drive did not (r = -0.19, P = 0.27). We also found that the resting PETCO2 was significantly and positively correlated with the OAHI, suggesting that high OAHI values were associated with resting CO2 retention. In awake children the OAHI correlates inversely with the hypoxic ventilatory drive and positively with the resting PETCO2. Whether or not diminished hypoxic drive or resting CO2 retention while awake can explain the severity of sleep-disordered breathing in this population is uncertain, but a reduced hypoxic ventilatory drive and resting CO2 retention are associated with sleep-disordered breathing in 6–12 year old children.
Dawoon Jung - One of the best experts on this subject based on the ideXlab platform.
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Apnea Hypopnea Index prediction using electrocardiogram acquired during the sleep onset period
IEEE Transactions on Biomedical Engineering, 2017Co-Authors: Dawoon Jung, Su Hwan Hwang, Doun Jeong, Kwang Suk ParkAbstract:The most widely used methods for predicting obstructive sleep Apnea are based on clinical or anatomico-functional features. To improve exactitude in obstructive sleep Apnea screening, this study aimed to devise a new predictor of Apnea–Hypopnea Index. We hypothesized that less irregular respiration cycles would be observed in the patients with more severe obstructive sleep Apnea during the sleep-onset period. From each of the 156 and 70 single-lead electrocardiograms collected from the internal polysomnographic database and from the Physionet Apnea-ECG database, respectively, the 150-s sleep-onset period was determined and the respiration cycles during this period were detected. Using the coefficient of variation of the respiration cycles, obtained from the internal dataset, as a predictor, the Apnea–Hypopnea Index predictive model was developed through regression analyses and k-fold cross-validations. The Apnea–Hypopnea Index predictability of the regression model was tested with the Physionet Apnea-ECG database. The regression model trained and validated from the 143 and 13 data, respectively, produced an absolute error (mean ± SD) of $3.65 \pm 2.98$ events/h and a Pearson's correlation coefficient of 0.97 ( P < 0.01) between the Apnea–Hypopnea Index predictive values and the reference values for the 70 test data. The new predictor of Apnea–Hypopnea Index has the potential to be utilized in making more reasoned clinical decisions on the need for formal diagnosis and treatment of obstructive sleep Apnea. Our study is the first study that presented the strategy for providing a reliable Apnea–Hypopnea Index without overnight recording.
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Apnea-Hypopnea Index prediction through an assessment of autonomic influence on heart rate in wakefulness
Physiology & behavior, 2016Co-Authors: Dawoon Jung, Doun Jeong, Yu Jin Lee, Kwang Suk ParkAbstract:Abstract With the high prevalence of obstructive sleep Apnea, the issue of developing a practical tool for obstructive sleep Apnea screening has been raised. Conventional obstructive sleep Apnea screening tools are limited in their ability to help clinicians make rational decisions due to their inability to predict the Apnea-Hypopnea Index. Our study aimed to develop a new prediction model that can provide a reliable Apnea-Hypopnea Index value during wakefulness. We hypothesized that patients with more severe obstructive sleep Apnea would exhibit more attenuated waking vagal tone, which may result in lower effectiveness in decreasing heart rate as a response to deep inspiration breath-holding. Prior to conducting nocturnal in-laboratory polysomnography, 30 non-obstructive sleep Apnea (Apnea-Hypopnea Index k -fold cross-validation tests were performed to develop an Apnea-Hypopnea Index prediction model. For the remaining 92 individuals, the developed model provided an absolute error (mean ± SD) of 3.53 ± 2.67 events/h and a Pearson's correlation coefficient of 0.99 ( P
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Apnea–Hypopnea Index Prediction Using Electrocardiogram Acquired During the Sleep-Onset Period
IEEE transactions on bio-medical engineering, 2016Co-Authors: Dawoon Jung, Su Hwan Hwang, Doun Jeong, Yu Jin Lee, Kwang Suk ParkAbstract:The most widely used methods for predicting obstructive sleep Apnea are based on clinical or anatomico-functional features. To improve exactitude in obstructive sleep Apnea screening, this study aimed to devise a new predictor of Apnea–Hypopnea Index. We hypothesized that less irregular respiration cycles would be observed in the patients with more severe obstructive sleep Apnea during the sleep-onset period. From each of the 156 and 70 single-lead electrocardiograms collected from the internal polysomnographic database and from the Physionet Apnea-ECG database, respectively, the 150-s sleep-onset period was determined and the respiration cycles during this period were detected. Using the coefficient of variation of the respiration cycles, obtained from the internal dataset, as a predictor, the Apnea–Hypopnea Index predictive model was developed through regression analyses and k-fold cross-validations. The Apnea–Hypopnea Index predictability of the regression model was tested with the Physionet Apnea-ECG database. The regression model trained and validated from the 143 and 13 data, respectively, produced an absolute error (mean ± SD) of $3.65 \pm 2.98$ events/h and a Pearson's correlation coefficient of 0.97 ( P < 0.01) between the Apnea–Hypopnea Index predictive values and the reference values for the 70 test data. The new predictor of Apnea–Hypopnea Index has the potential to be utilized in making more reasoned clinical decisions on the need for formal diagnosis and treatment of obstructive sleep Apnea. Our study is the first study that presented the strategy for providing a reliable Apnea–Hypopnea Index without overnight recording.
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Apnea-Hypopnea Index estimation using quantitative analysis of sleep macrostructure.
Physiological Measurement, 2016Co-Authors: Dawoon Jung, Su Hwan Hwang, Un Jeong, Kwang Suk ParkAbstract:Obstructive sleep Apnea, characterized by recurrent cessation or substantial reduction in breathing during sleep, is a prevalent and serious medical condition. Although a significant relationship between obstructive sleep Apnea and sleep macrostructure has been revealed in several studies, useful applications of this relationship have been limited. The aim of this study was to suggest a novel approach using quantitative analysis of sleep macrostructure to estimate the Apnea–Hypopnea Index, which is commonly used to assess obstructive sleep Apnea. Without being bound by conventional sleep macrostructure parameters, various new sleep macrostructure parameters were extracted from the polysomnographic recordings of 132 subjects. These recordings were split into training and validation sets, each with 66 recordings including 48 recordings with an Apnea–Hypopnea Index greater than 5 events h−1. The nonlinear regression analysis, performed using the percentage transition probability from non-rapid eye movement sleep stage 2 to stage 1, was most effective in estimating the Apnea–Hypopnea Index. Between the Apnea–Hypopnea Index estimates and the reference values reported from polysomnography, a root mean square error of 7.30 events h−1 was obtained in the validation set. At an Apnea–Hypopnea Index cut-off of ≥30 events h−1, the obstructive sleep Apnea diagnostic performance was provided with a sensitivity of 90.0%, a specificity of 93.5%, and an accuracy of 92.4% by our method. The developed Apnea–Hypopnea Index estimation model has the potential to be utilized in circumstances in which it is not possible to acquire or analyze respiration signal but it is possible to obtain information on sleep macrostructure.
